用旧硬件跑万亿模型，省钱又酷
有人用单张RTX 3060 12GB GPU和768GB二手Intel Optane持久内存运行了1万亿参数的Kimi K2.5模型，速度超过4 tokens/sec。Kimi K2.5是混合专家模型，总参数1T但每token仅激活32B。RTX 3060的12GB VRAM处理路由、注意力等延迟敏感部分，专家权重存储在Optane PMem中，192GB DDR4 ECC作为缓存。Optane PMem延迟比最佳NVMe SSD低很多，但比DRAM慢2-3倍。llama.cpp通过override-tensor标志调整张量放置，实现混合GPU/CPU推理。
Somebody just ran one trillion param model (Kimi K…
Somebody just ran one trillion param model (Kimi K2.5) on a single RTX 3060 12GB GPU at over 4 tokens/sec and 768GB of second-hand Intel Optane memory.
What happened is that a sparse model met an unusual memory tier that could hold its enormous body while the GPU handled the most time-sensitive organs.
i.e. the bulk of the sparse expert weights live in a larger, cheaper memory tier and are pulled into the computation as needed.
This worked because Kimi K2.5 is a Mixture-of-Experts model, so it has 1T total parameters but activates only 32B per token.
The RTX 3060’s 12GB VRAM holds latency-sensitive parts like routing, attention, dense layers, and shared experts.
The huge expert weights sit in Optane PMem, configured as RAM, while 192GB DDR4 ECC acts as cache.
He is using 6 Optane PMem (DCPMM) sticks. This retired memory format was made to bridge DRAM and SSD performance. The 768GB Optane configuration, using 6x128GB modules, does beat the best NVMe SSDs on latency by a wide margin, but remains 2x to 3x slower than DRAM.
llama.cpp handled hybrid GPU/CPU inference, with tensor placement tuned through flags like override-tensor.
The result was roughly 4 tokens/sec, which is slow for chat but impressive for a local 1T-parameter model on cheap retired enterprise hardware.
The DDR4 acted as cache, the Optane acted as a giant memory pool, and llama.cpp pushed routing and other critical tensors onto the 12GB GPU.